Research On Improved Whale Algorithm In Intelligent Control Of Mine Air Volume

Mine ventilation system is known as the lung of coal mine,its main task is to deliver fresh air to the underground places needing air,so as to ensure the safety of coal mine production and personal safety of miners.Therefore,it is crucial to enhance mine ventilation capacity and realize the on-demand air regulation of mine ventilation system for underground safe production,efficient mining and sustainable development.In this thesis,an intelligent control method of sensitive branch air volume based on intelligent optimization algorithm is proposed to meet the demand of air volume at the ventilation sites and achieve accurate on-demand control of air volume in the ventilation network.First of all,based on the study of the mine ventilation network and the basic theory of air volume regulation,this thesis proposes to take the air demand of the underground air branch as the optimization objective,combined with the basic law of air volume distribution,the operation condition of the fan and the mine air demand and other constraints,establish the nonlinear constrained model of air volume regulation,and then use the accurate penalty function method to de-constrain,simplify the calculation and solution of the model.Modeling of air volume optimization and regulation of ventilation network was completed.Secondly,according to the sensitivity theory of wind network and the nature of sensitivity matrix,the location of wind speed sensor is optimized,and the pretreatment technology of monitoring data is increased to improve the accuracy of monitoring data.Based on the sensitivity matrix of the wind network,the most effective regulation branch set is selected,and the reasonable range of the wind resistance can be adjusted is confirmed according to the sensitivity attenuation characteristics,so as to generate the sensitive branch air volume regulation scheme of the mine ventilation network.Thirdly,Whale Optimization Algorithm(WOA)was used to optimize the air volume objective function.A Multi-Strategy Whale Optimization Algorithm(MSWOA)was proposed to solve the problems of whale optimization algorithm falling into local optimality and slow convergence,including chaotic opposition learning initialization strategy,golden sine position updating strategy,nonlinear convergence factor and adaptive weight factor strategy,and diversity variation perturbation strategy.Through comparative analysis experiments with other improved WOA algorithms and single improved strategy,it is verified that MSWOA has superior optimization performance.Finally,based on the mine air volume intelligent control experiment platform,the sensitive branch air volume control scheme is verified experimentally.The overall structure of the system is designed,and relevant functional modules are developed.After solving the target air demand requirements and four sensitive branch air regulation schemes,MSWOA is invoked to optimize the target air volume,and the optimal air regulation scheme and specific air regulation parameters are output,so as to precisely control the air regulation executive device.The feasibility and effectiveness of the control scheme based on sensitive branch air volume are successfully verified,which provides a new decision support and method for intelligent air regulation of mine,and has practical significance for mine ventilation safety.This thesis has 45 figures,13 tables and 101 references.